{
  "abstract": "We introduce a hardware circuit model that implements spike-time dependent plasticity (STDP) to endow spiking neural networks with learning capabilities. Our circuit model is characterized as both minimal and bio-inspired, due to its simplicity and to a novel active dendrite compartment that mimics the synaptic potentiation mechanism. The active dendrite consists of an integrate and-fire stage which produces a train of pulses whose number is inversely related to the timing between pre- and post-synaptic spikes. The dendrite pulses modulate in a reliable manner the synaptic efficacy (conductance) that we implemented with a digipot, considered as an idealized non-volatile memristor. We demonstrate the behavior of the circuit by implementing a minimal spiking neuron model of associative learning by STDP, which is analog to the classic conditioning experiment of Pavlov’s dog.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "Adrien D’hollande"
    },
    {
      "name": "Olivier Schneegans"
    },
    {
      "name": "Kang Wang"
    },
    {
      "name": "Marcelo Rozenberg"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3gz4pfdxesaem.bib",
  "cited_by": 0,
  "doi": "10.1088/2634-4386/ae943a",
  "fulltext_url": null,
  "id": "3gz4pfdxesaem",
  "issns": [
    "2634-4386"
  ],
  "landing_url": "https://centralesupelec.hal.science/hal-05765227v1",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.phys"
    ],
    [
      "openalex",
      "2208|Advanced Memory and Neural Computing"
    ]
  ],
  "openalex": "W7172304820",
  "pdf_url": "https://centralesupelec.hal.science/hal-05765227/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-24",
  "publisher": "IOPScience",
  "ris": "https://research.draconitic.com/paper/3gz4pfdxesaem.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:41Z",
      "native_id": "hal-05765227",
      "source": "hal",
      "url": "https://centralesupelec.hal.science/hal-05765227v1"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "hal:0.phys",
      "subfield": "general"
    }
  ],
  "title": "Bio-inspired minimal hardware implementation of spike-time dependent plasticity for intelligent spiking neural networks",
  "title_text": "Bio-inspired minimal hardware implementation of spike-time dependent plasticity for intelligent spiking neural networks",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:41Z",
  "url": "https://research.draconitic.com/paper/3gz4pfdxesaem",
  "venue": "Neuromorphic Computing and Engineering",
  "versions": []
}